Search Results for author: Chen Henry Wu

Found 12 papers, 9 papers with code

PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting

no code implementations12 Apr 2023 Saman Motamed, Jianjin Xu, Chen Henry Wu, Fernando de la Torre

By using ~40 reference images, PATMAT creates anchor points in MAT's style module, and tunes the model using the fixed anchors to adapt the model to a new face identity.

Facial Inpainting

Zero-shot Model Diagnosis

no code implementations CVPR 2023 Jinqi Luo, Zhaoning Wang, Chen Henry Wu, Dong Huang, Fernando de la Torre

Extensive experiments demonstrate that our method is capable of producing counterfactual images and offering sensitivity analysis for model diagnosis without the need for a test set.


Semantic Image Attack for Visual Model Diagnosis

no code implementations23 Mar 2023 Jinqi Luo, Zhaoning Wang, Chen Henry Wu, Dong Huang, Fernando de la Torre

Rather than relying on a carefully designed test set to assess ML models' failures, fairness, or robustness, this paper proposes Semantic Image Attack (SIA), a method based on the adversarial attack that provides semantic adversarial images to allow model diagnosis, interpretability, and robustness.

Adversarial Attack Fairness +1

Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance

2 code implementations11 Oct 2022 Chen Henry Wu, Fernando de la Torre

The commonly-adopted formulation of the latent code of diffusion models is a sequence of gradually denoised samples, as opposed to the simpler (e. g., Gaussian) latent space of GANs, VAEs, and normalizing flows.

Image-to-Image Translation

Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models

1 code implementation14 Sep 2022 Chen Henry Wu, Saman Motamed, Shaunak Srivastava, Fernando de la Torre

Our experiments demonstrate how PromptGen can efficiently sample from several unconditional generative models (e. g., StyleGAN2, StyleNeRF, diffusion autoencoder, NVAE) in a controlled or/and de-biased manner using various off-the-shelf models: (1) with the CLIP model as control, PromptGen can sample images guided by text, (2) with image classifiers as control, PromptGen can de-bias generative models across a set of attributes or attribute combinations, and (3) with inverse graphics models as control, PromptGen can sample images of the same identity in different poses.

Selective Annotation Makes Language Models Better Few-Shot Learners

1 code implementation5 Sep 2022 Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, Tao Yu

Departing from recent in-context learning methods, we formulate an annotation-efficient, two-step framework: selective annotation that chooses a pool of examples to annotate from unlabeled data in advance, followed by prompt retrieval that retrieves task examples from the annotated pool at test time.

Code Generation Retrieval

EVA: An Open-Domain Chinese Dialogue System with Large-Scale Generative Pre-Training

2 code implementations3 Aug 2021 Hao Zhou, Pei Ke, Zheng Zhang, Yuxian Gu, Yinhe Zheng, Chujie Zheng, Yida Wang, Chen Henry Wu, Hao Sun, Xiaocong Yang, Bosi Wen, Xiaoyan Zhu, Minlie Huang, Jie Tang

Although pre-trained language models have remarkably enhanced the generation ability of dialogue systems, open-domain Chinese dialogue systems are still limited by the dialogue data and the model size compared with English ones.

NAST: A Non-Autoregressive Generator with Word Alignment for Unsupervised Text Style Transfer

1 code implementation Findings (ACL) 2021 Fei Huang, Zikai Chen, Chen Henry Wu, Qihan Guo, Xiaoyan Zhu, Minlie Huang

First, we observe that most words in the transferred sentence can be aligned with related words in the source sentence, so we explicitly model word alignments to suppress irrelevant words.

Style Transfer Text Style Transfer +2

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